Derivation of progressively variant dark data utility

Abstract

In an approach for derivation of progressively variant dark data utility for legacy system candidate microservices, a processor analyzes a variability of data stored in a legacy system for a plurality of data metrics. A processor measures, for each data metric, a utility of the data using an intra-analysis and a meta-analysis. A processor correlates the plurality of data metrics to candidate microservices. A processor generates insights for the candidate microservices based on the utility of the data.

What is claimed is: 
     
       1. A computer-implemented method comprising:
 analyzing, by one or more processors, variability of data stored in a legacy system for a plurality of data metrics; 
 measuring, by the one or more processors, for each data metric, a utility of the data using an intra-analysis and a meta-analysis; 
 correlating, by the one or more processors, the plurality of data metrics to candidate microservices; and 
 generating, by the one or more processors, insights for the candidate microservices based on the utility of the data by providing recommendations for which candidate microservices will capitalize on the data that would otherwise go dark through exposure as a microservice, wherein each recommendation is in a form of a scored weighting that includes a derived value of the data exposed by a given candidate microservice and a derived lifespan of the data exposed by the given candidate microservice. 
 
     
     
       2. The computer-implemented method of  claim 1 , wherein the plurality of data metrics includes transaction volume and data throughput. 
     
     
       3. The computer-implemented method of  claim 1 , wherein analyzing the variability of the data stored in the legacy system for the plurality of data metrics comprises:
 applying, by the one or more processors, bidirectional recurrent metric analysis to establish cosine similarities between two microservices. 
 
     
     
       4. The computer-implemented method of  claim 1 , wherein the intra-analysis is the utility of the data within the legacy system and the meta-analysis is the utility of the data across an organization from all data sources. 
     
     
       5. The computer-implemented method of  claim 1 , wherein correlating the plurality of data metrics to the candidate microservices comprises:
 analyzing, by the one or more processors, a given candidate microservice to derive which data metrics the a given candidate microservice will expose; 
 for each data metric exposed by the candidate microservice, retrieving, by the one or more processors, a cumulative data utility score of the candidate microservice; and 
 tallying, by the one or more processors, the cumulative data utility score for each data metric exposed in the given candidate microservice to rate an overall useful lifespan of the data exposed by the candidate microservice. 
 
     
     
       6. The computer-implemented method of  claim 5 , wherein the cumulative data utility score includes an intra-analysis data utility score and a meta-analysis data utility score, and wherein the intra-analysis data utility score is a first expected useful lifespan of a given data metric as the given data metric is used within the legacy system and the meta-analysis data utility score is a second expected useful lifespan of a given data metric as the given data metric is observed or forecasted to be used in systems beyond the legacy system. 
     
     
       7. A computer program product comprising:
 one or more computer readable storage media and program instructions collectively stored on the one or more computer readable storage media, the stored program instructions comprising: 
 program instructions to analyze variability of data stored in a legacy system for a plurality of data metrics; 
 program instructions to measure, for each data metric, a utility of the data using an intra- analysis and a meta-analysis; 
 program instructions to correlate the plurality of data metrics to candidate microservices; and 
 program instructions to generate insights for the candidate microservices based on the utility of the data by providing recommendations for which candidate micro services will capitalize on the data that would otherwise go dark through exposure as a microservice, wherein each recommendation is in a form of a scored weighting that includes a derived value of the data exposed by a given candidate microservice and a derived lifespan of the data exposed by the given candidate microservice. 
 
     
     
       8. The computer program product of  claim 7 , wherein the plurality of data metrics includes transaction volume and data throughput. 
     
     
       9. The computer program product of  claim 7 , wherein the program instructions to analyze the variability of the data stored in the legacy system for the plurality of data metrics comprise:
 program instructions to apply bidirectional recurrent metric analysis to establish cosine similarities between two microservices. 
 
     
     
       10. The computer program product of  claim 7 , wherein the intra-analysis is the utility of the data within the legacy system and the meta-analysis is the utility of the data across an organization from all data sources. 
     
     
       11. The computer program product of  claim 7 , wherein the program instructions to correlate the plurality of data metrics to the candidate microservices comprise:
 program instructions to analyze a given candidate microservice to derive which data metrics the a given candidate microservice will expose; 
 for each data metric exposed by the candidate microservice, program instructions to retrieve a cumulative data utility score of the candidate microservice; and 
 program instructions to tally the cumulative data utility score for each data metric exposed in the given candidate microservice to rate an overall useful lifespan of the data exposed by the candidate microservice. 
 
     
     
       12. The computer program product of  claim 11 , wherein the cumulative data utility score includes an intra-analysis data utility score and a meta-analysis data utility score, and wherein the intra-analysis data utility score is a first expected useful lifespan of a given data metric as the given data metric is used within the legacy system and the meta-analysis data utility score is a second expected useful lifespan of a given data metric as the given data metric is observed or forecasted to be used in systems beyond the legacy system. 
     
     
       13. A computer system comprising:
 one or more computer processors; 
 one or more computer readable storage media; 
 program instructions collectively stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the stored program instructions comprising: 
 program instructions to analyze variability of data stored in a legacy system for a plurality of data metrics; 
 program instructions to measure, for each data metric, a utility of the data using an intra-analysis and a meta-analysis; 
 program instructions to correlate the plurality of data metrics to candidate microservices; and 
 program instructions to generate insights for the candidate microservices based on the utility of the data by providing recommendations for which candidate micro services will capitalize on the data that would otherwise go dark through exposure as a microservice, wherein each recommendation is in a form of a scored weighting that includes a derived value of the data exposed by a given candidate microservice and a derived lifespan of the data exposed by the given candidate microservice. 
 
     
     
       14. The computer system of  claim 13 , wherein the plurality of data metrics includes transaction volume and data throughput. 
     
     
       15. The computer system of  claim 13 , wherein the program instructions to analyze the variability of the data stored in the legacy system for the plurality of data metrics comprise:
 program instructions to apply bidirectional recurrent metric analysis to establish cosine similarities between two microservices. 
 
     
     
       16. The computer system of  claim 13 , wherein the program instructions to correlate the plurality of data metrics to the candidate microservices comprise:
 program instructions to analyze a given candidate microservice to derive which data metrics the a given candidate microservice will expose; 
 for each data metric exposed by the candidate microservice, program instructions to retrieve a cumulative data utility score of the candidate microservice; and 
 program instructions to tally the cumulative data utility score for each data metric exposed in the given candidate microservice to rate an overall useful lifespan of the data exposed by the candidate microservice. 
 
     
     
       17. The computer system of  claim 16 , wherein the cumulative data utility score includes an intra-analysis data utility score and a meta-analysis data utility score, and wherein the intra-analysis data utility score is a first expected useful lifespan of a given data metric as the given data metric is used within the legacy system and the meta-analysis data utility score is a second expected useful lifespan of a given data metric as the given data metric is observed or forecasted to be used in systems beyond the legacy system.